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A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. It is a non-linear non-Gaussian estimation problem, typically requiring numerical...
Track before detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Under TkBD the single frame detector is removed and the tracker is supplied with the whole sensor image. Detection decisions are then shifted to the output of the tracker which is able to use temporal correlation to improve...
A solution to the problem of sensor bias estimation is presented for a multiple target scenario with asynchronous sensors. Expectation maximisation is used to decouple the target state and sensor bias estimation problems by treating the target states as missing data. The approach is compared with the EX method, which solves the bias estimation problem by using differences between measurements from...
A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point-measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TkBD problem. This paper...
In tracking scenarios with high target density, it is possible that two (or more) targets may be close enough to be unresolved by the sensor. In this case, the unresolved targets produce only a single measurement, and this may cause the tracking algorithm to incorrectly terminate track on one of the targets. This paper presents an extension of the probabilistic multi-hypothesis tracker (PMHT) to allow...
Tracking of dim, or low signal-to-noise ratio (SNR), targets is commonly achieved using track-before-detect (TBD) techniques. While traditional tracking algorithms operate on detections, typically formed by applying an intensity threshold to the sensor data, TBD algorithms operate directly on un-thresholded sensor data. Increasing the information available to the tracker in this way potentially allows...
Traditional tracking algorithms for video use object extraction to generate point measurements on targets distributed over several pixels. A probabilistic multi-hypothesis tracker based algorithm is demonstrated that simply uses thresholded images and performs better than the extraction based approach.
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